Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

The organization of behavior: A neuropsychological theory

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning , which drastically reduces the number of task-speciﬁc training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model (PaLM). We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly eﬃcient training across multiple TPU Pods. We demonstrate continued beneﬁts of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the ﬁnetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A signiﬁcant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.

PaLM: Scaling Language Modeling with Pathways

Minimal recursion semantics (MRS) is a framework for computational semantics that is suitable for parsing and generation and that can be implemented in typed feature structure formalisms. We discuss why, in general, a semantic representation with minimal structure is desirable and illustrate how a descriptively adequate representation with a nonrecursive structure may be achieved. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. We have integrated MRS with a broad-coverage HPSG grammar.

/pdf/minimal-recursion-semantics-an-introduction-4hdjxn0v8q.pdf

Minimal Recursion Semantics An Introduction

From Discourse to Logic

We present a method for quickly spotting overgeneration suspects (i.e., likely cause of overgeneration) in hand-coded grammars. The method is applied to a medium size Tree Adjoining Grammar (TAG) for French and is shown to help reduce the number of outputs by 70% almost all of it being overgeneration.

/pdf/spotting-overgeneration-suspects-409jfp5crj.pdf

Spotting Overgeneration Suspects

In this paper, we present an experiment designed to evaluate I-Fleg, a serious game prototype for second language acquisition we implemented in our laboratory. Specifically, our experiment focuses on teaching and training French elementary vocabulary to German primary school children.

Learning a Second Language with a Videogame

In this paper we propose a new shared task, KBGen, where the aim is to produce coherent descriptions of concepts and relationships in a frame-based knowledge base (KB). We propose to use the AURA knowledge base for the shared task which contains information about biological entities and processes. We describe how the AURA KB provides an application context for NLG and illustrate how this application context generalizes to other biology KBs. We argue that the easy availability of input data and a research community -- both domain experts and knowledge representation experts -- which actively uses these knowledge bases, along with regular evaluation experiments, creates an ideal scenario for a shared task.

KBGen -- Text Generation from Knowledge Bases as a New Shared Task

In parsing with Tree Adjoining Grammar TAG, independent derivations have been shown by Schabes and Shieber 1994 to be essential for correctly supporting syntactic analysis, semantic interpretation, and statistical language modeling. However, the parsing algorithm they propose is not directly applicable to Feature-Based TAGs FB-TAG. We provide a recognition algorithm for FB-TAG that supports both dependent and independent derivations. The resulting algorithm combines the benefits of independent derivations with those of Feature-Based grammars. In particular, we show that it accounts for a range of interactions between dependent vs. independent derivation on the one hand, and syntactic constraints, linear ordering, and scopal vs. nonscopal semantic dependencies on the other hand.

/pdf/multiple-adjunction-in-feature-based-tree-adjoining-grammar-4rue0yty6u.pdf

Multiple adjunction in feature-based tree-adjoining grammar

A difficult task when generating text from knowledge bases (KB) consists in finding appropriate lexicalisations for KB symbols. We present an approach for lexicalis-ing knowledge base relations and apply it to DBPedia data. Our model learns low-dimensional embeddings of words and RDF resources and uses these representations to score RDF properties against candidate lexicalisations. Training our model using (i) pairs of RDF triples and automatically generated verbalisations of these triples and (ii) pairs of paraphrases extracted from various resources, yields competitive results on DBPedia data.

/pdf/learning-embeddings-to-lexicalise-rdf-properties-4cj45hiiwt.pdf

Claire Gardent

Papers

Spotting Overgeneration Suspects

Learning a Second Language with a Videogame

KBGen -- Text Generation from Knowledge Bases as a New Shared Task

Multiple adjunction in feature-based tree-adjoining grammar

Learning Embeddings to lexicalise RDF Properties